aeon 0.5.0

A toolkit for conducting machine learning tasks with time series data

⌛ Welcome to aeon

aeon is an open-source toolkit for learning from time series. It is compatible with scikit-learn and provides access to the very latest algorithms for time series machine learning, in addition to a range of classical techniques for learning tasks such as forecasting and classification.

We strive to provide a broad library of time series algorithms including the latest advances, offer efficient implementations using numba, and interfaces with other time series packages to provide a single framework for algorithm comparison.

The latest aeon release is v0.5.0. You can view the full changelog here.

- The deprecation policy is currently suspended, be careful with the version bounds used when including aeon as a dependency.
- The policy will return in version v0.6.0, but in the mean time the suspension allows us to quickly develop and maintain the package in the forking transition period.

Our webpage and documentation is available at https://aeon-toolkit.org.

Overview
CI/CD
Code
Community

⚙️ Installation

aeon requires a Python version of 3.8 or greater. Our full installation guide is available in our documentation.

The easiest way to install aeon is via pip:

pip install aeon

Some estimators require additional packages to be installed. If you want to install the full package with all optional dependencies, you can use:

pip install aeon[all_extras]

Instructions for installation from the GitHub source can be found here.

⏲️ Getting started

The best place to get started for all aeon packages is our getting started guide.

Below we provide a quick example of how to use aeon for forecasting and classification.

Forecasting

import pandas as pd
from aeon.forecasting.trend import TrendForecaster

y = pd.Series([20.0, 40.0, 60.0, 80.0, 100.0])
>>> 0     20.0
>>> 1     40.0
>>> 2     60.0
>>> 3     80.0
>>> 4    100.0
>>> dtype: float64

forecaster = TrendForecaster()
forecaster.fit(y)  # fit the forecaster
>>> TrendForecaster()

pred = forecaster.predict(fh=[1, 2, 3])  # forecast the next 3 values
>>> 5    120.0
>>> 6    140.0
>>> 7    160.0
>>> dtype: float64

Classification

import numpy as np
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier

X = [[[1, 2, 3, 4, 5, 5]],  # 3D array example (univariate)
     [[1, 2, 3, 4, 4, 2]],  # Three samples, one channel, six series length,
     [[8, 7, 6, 5, 4, 4]]]
y = ['low', 'low', 'high']  # class labels for each sample
X = np.array(X)
y = np.array(y)

clf = KNeighborsTimeSeriesClassifier(distance="dtw")
clf.fit(X, y)  # fit the classifier on train data
>>> KNeighborsTimeSeriesClassifier()

X_test = np.array(
    [[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
y_pred = clf.predict(X_test)  # make class predictions on new data
>>> ['low' 'high' 'high']

💬 Where to ask questions

Type	Platforms
🐛 Bug Reports	GitHub Issue Tracker
✨ Feature Requests & Ideas	GitHub Issue Tracker & Slack
💻 Usage Questions	GitHub Discussions & Slack
💬 General Discussion	GitHub Discussions & Slack
🏭 Contribution & Development	Slack